Using a complex systems approach to model and analyse air transportation

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ComplexWorld PhD

PhD Candidate: Soufiane Bouarfa (TU Delft) PhD Supervisors: prof. Henk Blom and prof. R. Curran (TU Delft)

Motivation

The resilience of the current air transportation system is implicitly tested around the globe on a regular basis. Each day of operation, the system is subject to a multitude of disruptions ranging from deteriorating weather, through passenger delays, up to aircraft or crew related problems. Current practice consists of an effective coordination process between human operators who play a key role in recovering from disruptions. Motivated by the need to understand such a human-invoked resilience, this research explores a multi-agent systems approach to model part of the socio-technical air transportation system. The exemplar focus is on Airline Operations Control (AOC) where coordination between humans facilitate disruption recovery

Coordination is a unique capability by humans that plays an essential role in recovering from disruptions. Klein [1] defines coordination as “the attempt by multiple entities to act in concert in order to achieve a common goal by carrying out a script they all understand.” Within AOC, many operators with different roles interact and coordinate at the sharp edge towards achieving a common goal, namely making sure their airline operations adhere to the plan as close as possible. Consideration of the aircraft routings, crew, maintenance, weather, customer needs, and turnaround processes complicate AOC. Current practice consists of an effective coordination process between humans who play an essential role in disruption management. In order to start thinking about a further optimization of AOC, a prerequisite is to first develop an in-depth understanding of current coordination processes.

Research Aim

This research aims at agent-based modelling and simulating an AOC system using various coordination strategies. We embrace Agent-Based Modeling and Simulation (ABMS) because it has been extensively used to model and analyze large-scale complex socio-technical systems, and address cases where agents need to coordinate and solve problems in a distributed fashion [2][3][4][5]. ABMS provides a platform to integrate multiple heterogeneous components at different levels. Models of actors, technological systems, and the operating environment as well as the interactions between them can be naturally covered. Therefore, it is expected that an ABMS approach will a) help predicting the performance of the complex coordinated AOC socio-technical system that emerges from the interactions between AOC operators; and b) help manage dependencies between the activities of AOC operators.

Related Work

In the literature, there are few studies on AOC socio-technical decision-making and coordination. Bruce [6][7] has examined many aspects of decision-making by airline controllers through conducting multiple case studies at six AOC centers. Feigh [8] has examined the work of airline controllers at four US airlines of varying sizes, and applied an ethnographic approach to have representative work models. Pujet and Feron [9] have proposed a discrete event model to investigate the dynamic behavior of the AOC center of a major airline. In their model, each agent was represented as a multi-class queuing server, and the AOC as a multi-agent, multi-class queuing system. Most often studies of AOC mainly focus on developing tools for solving operational problems. e.g. [10][11][12] Nevertheless current practice of AOC is to coordinate disruptions manually rather than relying on coordination tools.

Applications

The model can be used to assess the effectiveness of airline disruption management plans and improve decision-making by airline controllers.

Publications

  • S. Bouarfa, H. A. P. Blom, R. Curran, M. H. C. Everdij, “Agent-based modeling and simulation of emergent behavior in air transportation,” Complex Adaptive Systems Modeling, 1:15, 2013. http://www.casmodeling.com/content/1/1/15
  • S. Bouarfa, H.A.P. Blom, R. Curran, “Airport Performance Modeling using an Agent-Based Approach,” in Proceedings Air Transport and Operations Seminar (ATOS 2012), Eds. R. Curran et al., p 427-442, 2012.
  • S. Bouarfa, H.A.P. Blom, R. Curran, K. Hindriks, “A study into modelling coordination in disruption management by Airline Operations Control,” 2014 Aviation Technology, Integration, and Operations Conference, American Institute of Aeronautics and Astronautics.

References

  1. 1. G. Klein, “Features of team coordination,” in New Trends in Cooperative Activities: Understanding System Dynamics in Complex Environments, M. McNeese, M. R. Endsley & E. Salas, Eds. HFES, Santa Monica, 2001, pp. 68-95.
  2. A.P. Shah, A. R. Pritchett, K. M. Feigh, S. A. Kalaver, A. Jadhav, K. M. Corker, D. M. Holl, R. C. Bea. (2005, June). Analyzing air traffic management systems using agent-based modeling and simulation. Presented at the 6th USA/Europe ATM R&D Seminar. [Online]. Available: http://www.atmseminar.org/seminarContent/seminar6/papers/p_129_IAC.pdf
  3. S. Stroeve, H. Blom, M. van der Park. (2003, June). Multi-Agent Situation Awareness Error Evolution in Accident Risk Modeling. Presented at the 5th USA/Europe Air Traffic Management R&D Seminar. [online]. http://www.atmseminar.org/seminarContent/seminar5/papers/p_067_S.pdf
  4. S. Wolfe, P. Jarvis, F. Enomoto, M. Sierhuis, B. Putten, “A Multi-Agent Simulation of Collaborative Air Traffic Flow Management”, in Multi-Agent Systems for traffic and transportation engineering, A. Bazzan, F. Klugl, Eds. Hershey: IGI Global, 2009, pp 357-381.
  5. B. Chen, H. Cheng, “A review of the applications of agent-technology in traffic and transportation systems,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 2, pp. 485-497, June 2010.
  6. P. Bruce, Understanding Decision-Making Processes in Airline Operations Control, Ashgate Publishing Company, Burlington, USA, 2011.
  7. 7. P. J. Bruce. (2011, January). Decision-making in airline operations: the importance of identifying decision considerations. Internal Journal of Aviation. Vol. 1, Nos. 1/2. pp 89-104. Available: http://inderscience.metapress.com/content/m34750h347u85401/
  8. K. M. Feigh, “Design of cognitive work support systems for airline operations,” Ph.D. dissertation, Dept. Industrial and Systems Engineering. Georgia Institute of Technology, Atlanta, GA, 2008.
  9. N. Pujet, E. Feron. (1998, December). Modelling an airline operations control. Presented at the 2nd USA/Europe Air Traffic Management R&D Seminar. [online]. Available: http://atmseminar.org/seminarContent/seminar2/papers/p_034_APMMA.pdf
  10. S. Bratu, C. Barnhart. (2006, June). Flight operations recovery: New approaches considering passenger recovery. Journal of Scheduling. Vol. 9, issue 3, pp. 279-298. Available http://link.springer.com/article/10.1007/s10951-006-6781-0
  11. 11. K. F. Abdelghany. A. F. Abdelghany. and G. Ekollu. (2008, March). An Integrated Decision-Support Tool for Airlines Schedule Recovery during Irregular Operations. European Journal of Operational Research. 185(2). pp. 825-848. Available: http://www.sciencedirect.com/science/article/pii/S0377221707000835
  12. 12. S. C. Grandeau, M. D. Clarke, D. F. X. Mathaisel, “The processes of airline system operations control,” in Airline Systems Operations Control, ed. G. Yu, Kluwer Academic Publishers Group, 1998, pp. 312-369.
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